The present invention relates to a method or system used to predict the buzz, squeak, rattle, and/or other noise source characteristics (collectively “BSR source characteristics”, “noise source characteristics”, “BSR characteristics”, or simply “noise characteristics”) for an assembly of one or more components, with each component comprising one or more parts. More specifically, the invention relates to a method or system that receives design information regarding an assembly of components and/or parts in the form of a finite element analysis or model; selectively identifies a subset of points that are the most relevant with respect to noise source characteristics from all of the points in the analysis or model; and evaluates the relevant characteristics at those subset of points, using the displacements, velocities, loads, or other attributes relating to noise source characteristics at those points.
Structural noise is a significant problem in many multi-part or multi-component assemblies, especially for applications in the automotive and aerospace industries. Such noise characteristics are almost always undesirable, but the current art does not provide an analytical tool to effectively predict noise source characteristics.
Structurally borne noises can originate at the locations of various fasteners, such as bolts, welds, snaps, glue, rivets, screws, nails, or any other fastener or connector (collectively “fasteners”), as well as at points on the design where a fastener is not present. Noise source characteristics often originate from relatively close gaps in a design that are subjected to dynamic loads. Noise characteristics including buzz, squeak, rattle, and other phenomena involve complex impact, friction, and damping and other noise dissipation mechanisms. Among other inadequacies, the present art does not provide an analytical means or process to mathematically predict or evaluate noise source characteristics in an accurate and comprehensive manner. Industry is left with physical testing as the unfortunate primary option for evaluating noise characteristics.
Physical tests are both expensive and time consuming. Moreover, physical testing will often result in too little information, in too late a time frame. Since a physical test often requires the actual manufacture of many assemblies for testing, noise-based design changes can only be made in the later stages of the product development and design process when such changes are most expensive and least convenient with respect to the product development schedule. By that point in time, the manufacturer will have already invested significant time and effort in a design that is not sufficiently robust for production. Making matters worse, the evaluation of the re-designed assembly may also have to wait until a sufficient number of assemblies can be manufactured.
Time and expense issues aside, physical testing suffers from other significant limitations. Assemblies often involve a potentially voluminous number of parts and components. Noise sources at internal locations are often not externally audible or identifiable from the outside, and thus are difficult to identify. Several iterations of testing may be required before such characteristics can be identified with adequate specificity. This is especially true when conducting a physical test in actual conditions, such as a road test for automobiles. Problems and challenges relating to physical testing make an analytical solution desirable outside of the time and expense involved. It is desirable for a system or method to apply analytical intelligence to the prediction and evaluation of noise source characteristics including buzz, squeak, rattle, and/or other phenomenon in a real-time manner. It is also beneficial if certain critical locations with respect to noise source characteristics in a design may be isolated from less important non-critical locations with relative ease and speed. Such a system or method may be used for product development, especially in earlier stages, without compromising the timeline and cost.
Another weakness of physical testing is that physical tests may need to be repeated in order to identify the correct timeline for multiple different instances of noise source characteristics in the design of an assembly. An analytical approach capable of identifying a subset of key data points out of the potentially voluminous number of representative data points in a model may not require such repetition. Such a subset of key data points would include the locations in a design with the most significant noise characteristics. By limiting the noise evaluation to only a subset of points, noise source characteristics for a design can be evaluated in a real-time manner. It is also desirable if a noise source evaluation system or method is able to prioritize the analysis relating to noise source characteristics to identify those locations in the design with the most significant noise characteristics.
Reliance on physical testing also requires that a sufficient number of physical samples are tested in order to generate statistically significant data. This is both time consuming and expensive, and it magnifies the other difficulties associated with physical testing because such testing must be repeated numerous times. It is desirable for an analytical approach to replace the need for such extensive physical testing. An analytical solution may also provide useful insights to the critical points in a design so that when physical testing is necessary, it can be done as efficiently and inexpensively as possible.
Industry heavily depends on the computer-aided engineering tools using finite element solvers, finite element modelers, and finite element processors (collectively “finite element analyzers”) for product development. The present invention recognizes the reliability, robustness, efficiency and cost-effectiveness of such a computational approach. It is desirable for a noise prediction system or method to interface with the cost-effective finite element analyzers, existing finite element models of assemblies, and other similar data and tools.
To the extent that noise characteristic analytical models exist in the current art, they do not provide a comprehensive analysis of noise characteristics as well as an analysis relating to the fasteners themselves. It is desirable for an analytical solution to provide comprehensive functionality with regards to predicting and evaluating sources of noise in a design. A fully automated and intelligent system is highly desirable to identify and resolve noise source characteristics and related issues in real-time. Structurally borne noise characteristics are ultimately generated at specific points or specific pairs of points. It may often be desirable for an analytical solution to have the capability of analyzing potential noise sources on a point-by-point or point-pair-by-point-pair basis, instead of being limited to using grids or other approaches. Building intelligence into a comprehensive and non-aggregated or overly generalized approach may also facilitate better and faster solutions.